AI in Treasury: So what?

By:

Enrico Camerinelli ([email protected]) Enrico Camerinelli

Ivan De Crescenzo ([email protected]) Ivan De Crescenzo

Tanya Kuznetsova ([email protected]) Tanya Kohen, CTP

Lee-Ann Perkins ([email protected]) Lee-Ann Perkins, FCT, MBA, CTP(CD)

Ignacio Sanchez Miret ([email protected]) Ignacio Sanchez-Miret

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The integration of AI in treasury represents both an opportunity and a challenge. While the technology offers significant potential for improving efficiency and decision-making, success depends on careful planning, clear objectives, and maintaining the right balance between automation and human oversight. Indeed, there is not a single task of a treasurer that cannot be improved with AI. As AI continues to evolve, treasury professionals who approach its implementation thoughtfully and strategically will be best positioned to realize its benefits while managing its risks effectively.

Introduction

The treasury landscape is experiencing a significant transformation with the emergence of artificial intelligence technologies. While AI has been present in various forms for years, recent advancements, particularly in generative AI, are creating new opportunities and challenges for treasury professionals. The vision of AI is fundamentally optimistic because, like any new technology, it can be used for both good and bad purposes, and it is proving to be an efficient assistant in forecasting and predictability capabilities as well as in processes such as reading, summarizing, extracting information, and presenting reports in day-to-day operations. As organizations navigate this technological evolution, understanding the implications and potential applications of AI becomes increasingly critical for modern treasury operations.

Why treasurers should care of artificial intelligence

Treasury professionals should pay close attention to AI developments as the technology promises to revolutionize many core treasury functions. As highlighted by industry experts, AI is not merely a buzzword but a transformative force that could fundamentally change how treasury departments operate. The technology’s ability to handle complex data analysis, automate routine tasks, and provide predictive insights aligns well with the evolving role of treasury from a primarily operational function to a more strategic business partner.

However, it’s important to note that while service providers increasingly incorporate “AI” into their applications, treasurers must look deeper into what this actually entails. In many cases, these solutions merely implement algorithmic rules that require user training, rather than delivering the self-learning capabilities that true AI promises. Despite these limitations, leveraging AI should be a priority for treasury professionals, particularly as treasury continues to evolve as a newer function requiring greater visibility for its contributions to company success.

Use cases, expected benefits, and mistakes to avoid

Treasury professionals highlight several practical applications where AI is making significant impact in their operations. In bank reconciliation, modern AI solutions can handle complex scenarios including multi-currency transactions and situations where single movements relate to multiple open positions. The field of fraud monitoring has seen remarkable advances, with companies like JPMorgan, Trustpair, BNP Paribas, or SisId demonstrating AI’s potential for enhancing security. Compliance and risk management applications, including KYC and AML solutions, show promise though regulatory challenges sometimes slow market adoption.

In the realm of liquidity management, AI systems excel at processing multiple data points across different currencies and timeframes simultaneously, converting complex decisions into actionable rules. Contract analysis and negotiation support has been transformed by AI’s ability to process multiple loan agreements simultaneously and identify previously accepted conditions. Cash flow forecasting has also seen significant improvements through AI’s capacity to integrate multiple data sources and historical patterns. While the use of internal and external data may have an immediate impact on enhancing the accuracy for short-term liquidity management, this is still questionable for long-term forecasting.

Treasury departments must carefully consider several key risks in their AI implementation journey. Data security stands as a primary concern, especially regarding the feeding of internal financial information into AI systems. Treasurers need assurance that AI solutions provide adequate protection for sensitive data. The non-deterministic nature of AI, particularly in generative applications where the same query might produce different results at different times, creates challenges for audit trails and accountability. The black box nature of machine learning algorithms necessitates careful oversight rather than blind reliance on outputs. There’s also a risk of over-reliance on AI systems without proper understanding of their underlying mechanisms.

So what?

For treasury professionals looking to implement AI solutions, successful implementation requires a systematic approach beginning with a well-defined vision and specific problem identification. Organizations must secure management support and necessary funding while ensuring proper technical expertise and data organization. Throughout this process, maintaining realistic expectations about implementation timeframes proves crucial.

Change management plays a vital role in successful AI adoption. As leaders of treasury functions, professionals must initiate open dialogue and demonstrate leadership in AI integration. Good communication and deliberate change management become essential as teams learn and adapt to new technologies and processes.

The generational aspect of AI adoption presents both opportunities and challenges. Younger professionals entering the treasury field view AI as a natural part of their toolkit, similar to how they view mobile phones as an integral part of daily life. This technological integration can help attract bright, energetic talent to treasury departments, though organizations must balance this enthusiasm with appropriate oversight and experience from senior Treasurers.

The right mindset proves crucial for successful AI implementation. Treasury professionals are encouraged to replace fear with excited curiosity and embrace opportunities for automation, innovation, and efficiency. Focus should remain on upskilling teams to benefit the company, customers, and careers while viewing AI as a tool to enhance human capabilities rather than replace human judgment.

As with any significant technological change, implementing AI solutions requires patience, careful planning, and ongoing refinement. Companies should be prepared for a journey that involves continuous learning and adaptation rather than expecting immediate perfect results. The key is to approach AI adoption with optimism while maintaining appropriate caution regarding data security and algorithmic reliability.